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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Northern Colorado Water Resources: Using Earth Observations to Assess Relationships between Snowpack and Wildfires for Water Utility Management

In recent years, wildfires in the western U.S. have increased in size, frequency, and severity. Colorado is no exception to this trend, with many recent wildfires raising concerns. The Cameron Peak and East Troublesome Fires, the two largest recorded wildfires in Colorado history, burned over 400,000 acres in 2020. Wildfires pose a significant threat to high-elevation ecosystems, especially in terms of disturbances to watershed resources. Colorado’s annual water supply is dependent upon the melt and runoff of high-elevation snowpack, thus understanding wildfire effects is crucial. This project partnered with Northern Water, a water management company in Colorado, to explore fire impacts on Colorado Front Range watersheds and water supply forecasts. We investigated the use of remote sensing to study the influences of wildfire on snow depth, as Northern Water had previously relied on field-based observations of snowpack for their water supply forecasts. This project utilized airborne LiDAR data from Airborne Snow Observatories, Inc., Landsat 8 OLI derived products, as well as in situ snow survey data from Colorado State University to assess and model changes in snowpack over time. We uncovered key drivers of change in snowpack such as burn severity and snow zone and created a random forest model to determine and investigate their relationships. Despite temporal data availability limitations, analysis also showed that remotely-sensed snow depth measurements were strongly correlated with in situ snow depth measurements, revealing the accuracy and feasibility of using remote sensing technologies to study landscape-scale snowpack characteristics for water utility management purposes.

airborne LiDAR↗
NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗
DOE OSTI2020

Probing and manipulating magnetic and 2D quantum materials using ultrafast laser and high harmonic sources

The ability to probe the full dynamic response of quantum materials on the length- and time-scales (Å to attoseconds on up) fundamental to charge, spin and phonon interactions is leading to a host of new discoveries. The coupled interactions between charges, spins, orbital and lattice degrees of freedom are key to determining the state of a material, whether metallic, insulating, superconducting or magnetic. Under thermal equilibrium conditions these states can be tuned by varying the temperature, pressure, chemical doping or dimensionality. However, their inherent complexity and rich phase landscape make them challenging to understand or manipulate deterministically. Fortunately, ultrafast light sources have undergone remarkable advances in recent years, achieving what was merely a dream three decades ago, i.e., full coherent control of light fields spanning the THz to the X-ray regions. Here, these new capabilities are providing powerful new tools for coherently manipulating and probing quantum materials using light. Recent advances in high harmonic generation (HHG) have provided new techniques for uncovering new phases and couplings in 2D and magnetic materials, using femtosecond lasers to coherently manipulate and tune the properties and interactions in the material, and HHG as a probe to map out these new phases and couplings. There are several beautiful recent observations of exciting new states in materials using light, but in some cases, the limited experimental data points made it challenging to validate an interpretation. The new ability of ultrafast HHG-based photoemission and magneto-optic spectroscopies to precisely probe multiple phases, electronic and magnetic order parameters, when combined with scanning the laser excitation fluence, is allowing us to map how material phases, magnetic states, and electron-phonon-spin couplings in these delicate materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Challenges in Automated Detection of COVID-19 Misinformation

The COVID-19 pandemic has made the dangers of the spread of misinformation obvious but despite much global effort to curbing its spread, fake information about the pandemic keeps proliferating. In this paper, we address the development of automated methods for verification of claims about COVID-19 and discuss the challenges associated with this task. We focus on labeled data collection, limitations of existing models, and difficulties of applying misinformation detection models in practical applications. Our initial analysis indicates label imbalance may be a particular challenge for developing claim verification models and we discuss options for alleviating this issue.

Herrmannova, Dasha↗
DOE OSTI2021

An Indicator-based Approach to Sustainable Management of Natural Resources (Chapter 12)

Assessing the sustainability of natural resource management choices for agricultural and forest lands requires quantification of potential changes to a set of environmental and socioeconomic indicators selected to characterize reference scenarios relative to projected future scenarios. Correctly framing the questions with local stakeholders is a critical first step in the sustainability assessment, and the questions that can be addressed are often limited by data availability. Selecting and prioritizing indicators with stakeholders to address their needs and concerns improves the likelihood of investment in monitoring and evaluation of those indicators over time. Computational techniques for analyzing interactions between the selected indicators are inherently affected by the scales and formats of the assembled indicator datasets. Data analytics have the potential to improve understanding of the potential synergies and tradeoffs involved with meeting multiple environmental and socioeconomic goals simultaneously, but timely and appropriate indicator datasets are not always available—even in this new era of “big data.” Continued improvements in data science and data analytics are needed to broaden understanding and acceptance of problems and to provide valuable information for natural resource management. Advances in these areas will enable society to design future landscapes that meet multiple objectives, including the provisioning of agricultural and forest resources along with a variety of ecosystem services (e.g., clean water and healthy soils).

Parish, Esther↗
DOE OSTI2022

Comparative assessment of neutron flux sensor technologies for advanced reactors

This report documents the comparative assessment of the rhodium-based self-powered neutron detectors (Rh-SPND), micro-pocket fission detectors (MPFD), and dosimetry wires in heated irradiations at the Neutron Radiography reactor facility at Idaho National Laboratory. The sensors performances are evaluated high-temperature environments (upwards of 850°C) during maximum reactor power to simulate use in advanced reactor applications. The performance of the Rh-SPNDs indicates the experiment cartridge heater power supply interferes with the SPND signals. This interference become increasingly significant at temperatures above 500°C with minimal interference observed for temperatures below 500°C. This work also demonstrated the fabrication process for the updated design of the MPFD, but issues related to the seal welds were identified and usable data was limited. Finally, the dosimetry measurements were within the expected range correlated with reactor power; thus, the dosimetry results were used to provide preliminary SPND calibration factors. The results from this experiment serves as a reference for developing and testing of flux sensors that are designed for high-temperature irradiations and advanced reactor deployments. This includes upcoming FY22 irradiations at the Massachusetts Institute of Technology Reactor and the Neutron Radiography (NRAD) reactor utilizing additional fission chambers from Photonis Technologies and fission chambers and SPNDs from The French Alternative Energies and Atomic Energy Commission.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Development of a high fidelity model of the CROCUS experimental reactor

Measurements of scalar flux distributions with fine spatial and energy resolutions are needed to remedy one of the validation shortcomings of the novel neutronics full core solvers, such as MPACT and nTRACER. While a very detailed resolution of the flux can be calculated with such codes, only a limited experimental data set is available to check their accuracy. Such type of measurements are on-going at the zero power reactor CROCUS, operated at the Laboratory for Reactor Physics and System Behaviours of the EPFL, thanks to the development of advanced miniature neutron detection systems. This kind of experimental data would provide the community with a suitable benchmark for the validation of high fidelity neutronics solvers. In parallel, a multi-physics solver for steady-state and transient analysis of nuclear reactors, named GeN-Foam, has been developed. Based on the finite-volume OpenFOAM library, GeN-Foam provides us with enough flexibility to analyze non-conventional reactor geometries such as that of CROCUS. While CROCUS heterogeneities cannot be modeled by MPACT and nTRACER for the moment, GeN-Foam offers a unique opportunity to build a high fidelity model which mimics these codes' method to reach sub-pin simulation resolution. This document aims at describing the work achieved to get from the existing GeN-Foam model of the CROCUS reactor based on a structured mesh and using the neutron diffusion, the first high-fidelity model using discrete ordinates method as an approximation to neutron transport and an unstructured mesh for inter lattice water gap description and sub-pin heterogeneous modeling. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Quantum Annealing for Real-World Machine Learning Applications

Optimizing the training of a machine learning pipeline is important for reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications have shown interesting results especially under the conditions where the performance of classical machine learning techniques are limited such as limited training data and high dimensional features. This chapter explores the application of D-Wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, security, computational biology, biomedical sciences, and physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

Kumar nath, Rajdeep↗
DOE OSTI2026

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

Das, Arghya Ranjan [Purdue U.] (ORCID:000000018451↗
DOE OSTI2020

Seismology in the Cloud: A New Streaming Workflow

Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

58 GEOSCIENCES↗
NASA NTRS2016

Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release)

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.

SMAP L4_SM↗
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

New Developments in Retropropulsion Testing for Mars Entry, Descent and Landing

NASA’s plans for landing human-scale payloads on Mars in the next decade require that retrorockets be used to decelerate the atmospheric entry vehicle continuously from supersonic conditions through soft touchdown. Conventional Mars entry vehicle architectures that include a single parachute for supersonic-to-subsonic descent are not scalable to the sizes needed to land humans on Mars (~20 metric tons). The major aerosciences risks are the uncertainties in predicting aerodynamic stability and performance during powered free-flight and landing. These risks are influenced partially by current limitations in relevant data and testing methods. Consequently, trajectory simulations currently depend on unvalidated powered descent and landing aerodynamics models. NASA engineers have identified gaps in testing methods that, if addressed, would improve the ability to validate these models. There are gaps in capabilities to test multi-engine hot-gas retropropulsion systems in US wind tunnels. This is partially due to the successful use of parachutes as decelerators for human spaceflight at Earth and for the entire Mars lander program to date. Retropulsion test data historically and to this day are limited to using high pressure air jets, at comparatively low temperatures,as engine plume simulants on subscale wind tunnel models. Additionally, the ability to directly measure aerodynamic interference force and moments is limited by existing flow-through balance capabilities. This paper briefly covers historical and recent test data, and identifies new ground test techniques as a means to provide more relevant test data for powered flight and landing aerodynamic model validation.These techniques include using heated inert gases as a substitute for combustion products, additively manufactured 6-component flow-through force and moment balances, and off-body quantitative diagnostic measurements.

Retropropulsion↗
DOE OSTI2021

Optimal Bayesian supervised domain adaptation for RNA sequencing data

Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online.

Biochemistry & Molecular Biology↗
NASA NTRS2011

Standardization of a Volumetric Displacement Measurement for Two-Body Abrasion Scratch Test Data Analysis

A limitation has been identified in the existing test standards used for making controlled, two-body abrasion scratch measurements based solely on the width of the resultant score on the surface of the material. A new, more robust method is proposed for analyzing a surface scratch that takes into account the full three-dimensional profile of the displaced material. To accomplish this, a set of four volume- displacement metrics was systematically defined by normalizing the overall surface profile to denote statistically the area of relevance, termed the Zone of Interaction. From this baseline, depth of the trough and height of the plowed material are factored into the overall deformation assessment. Proof-of-concept data were collected and analyzed to demonstrate the performance of this proposed methodology. This technique takes advantage of advanced imaging capabilities that allow resolution of the scratched surface to be quantified in greater detail than was previously achievable. When reviewing existing data analysis techniques for conducting two-body abrasive scratch tests, it was found that the ASTM International Standard G 171 specified a generic metric based only on visually determined scratch width as a way to compare abraded materials. A limitation to this method was identified in that the scratch width is based on optical surface measurements, manually defined by approximating the boundaries, but does not consider the three-dimensional volume of material that was displaced. With large, potentially irregular deformations occurring on softer materials, it becomes unclear where to systematically determine the scratch width. Specifically, surface scratches on different samples may look the same from a top view, resulting in an identical scratch width measurement, but may vary in actual penetration depth and/or plowing deformation. Therefore, two different scratch profiles would be measured as having identical abrasion properties, although they differ significantly.

Street, K. W. Jr.↗
NASA NTRS2006

Generation of Data-Rate Profiles of Ka-Band Deep-Space Links

A short report discusses a methodology for designing Ka-band Deep-Space-to- Earth radio-communication links. This methodology is oriented toward minimizing the effects of weather on the Ka-band telecommunication link by maximizing the expected data return subject to minimum link availability and a limited number of data rates. This methodology differs from the current standard practices in which a link is designed according to a margin policy for a given link availability at 10 elevation. In this methodology, one chooses a data-rate profile that will maximize the average data return over a pass while satisfying a minimum-availability requirement for the pass, subject to mission operational limitations expressed in terms of the number of data rates used during the pass. The methodology is implemented in an intelligent search algorithm that first finds the allowable data-rate profiles from the mission constraints, spacecraft-to-Earth distance, spacecraft EIRP (effective isotropic radiated power), and the applicable zenith atmospheric noise temperature distribution, and then selects the best data rate in terms of maximum average data return from the set of allowable data-rate profiles.

Shambayati, Shervin↗
NASA NTRS1991

Evaluation of Surlyn 8920 as PHE Visor Material and Evaluations of New Adhesives for Improving Bonding Between Teflon and Stainless Steel at Cryogenic Temperature

Two studies are presented, and in the first study, Surlyn 8920 (an ionic and amorphous low density polyethylene made by Dupont) was evaluated as a possible replacement of Plexyglass G as PHE visor material. Four formulations of the polymer were made by adding different amounts of UV stabilizer, energy quencher, and antioxident in a Brabender Plasticorder. The formulated polymers were molded in the form of sheets in a compression molder. Cut samples from the molded sheets were exposed in a weatherometer and tested on Instron Tensile Tester for strength and elongation. Specially molded samples of the formulated polymers were subjected to Charpy Impact Tests. In the second study, preliminary evaluations of adhesives for improvement of bonding between Teflon and stainless steel (SS) were performed. Kapton, a high temperature polyimide made by Dupont, and a rubber based adhesive made by Potter Paint Co., were evaluated against industrial quality epoxy, the current material used to bond Teflon and SS. The degreased surfaces of the SS discs were etched mechanically, with a few of these etched chemically. The surfaces of the SS discs were etched mechanically, with a few of these etched chemically. Bonding strengths were evaluated using lap shear tests on the Instron Tensile Tester for the samples bonded by Kapton and industrial quality epoxy. Bond strengths were also evaluated using a pull test on the Instron for the samples bonded by Potter adhesive (CWL-152) and industrial quality epoxy. Based on limited lap shear data, Kapton gave bond strength favorable compared to that of industrial epoxy. Based on limited pull test data, Kapton bonded and CWL-152 bonded samples showed poor strength compared to epoxy bonded sample.

Ray, Asit K.↗